ISCB SC RSG-Nigeria Webinar - When GWAS Meets Biochemistry
ISCB-SC RSG-Nigeria is excited to kick off our July Webinar with When GWAS Meets Biochemistry: Lessons from Metabolite Genetics.
Genome-Wide Association Studies (GWAS) routinely identify robust genetic associations, yet translating these signals into causal genes and mechanisms remains a major bottleneck. Metabolite GWAS offers a rare opportunity to study genetic associations in systems where the biology is often already known, due to the rich history of biochemical investigation. By leveraging this prior biological knowledge and orthogonal validation, such as merging genetic data with biochemical logic, we can move beyond simple associations to deduce complex causal mechanisms, even for intronic or intergenic variants. This session will explore how merging genetic data with biochemical logic allows us to uncover hidden mechanisms, helping researchers interpret disease GWAS results even when direct biological validation is not yet possible.
What you’ll learn from this Webinar:
Learn why the causal gene for a GWAS signal is, in most cases, the closest gene to the lead SNP.
Gain insights into how to use independent signals from metabolite GWAS and pQTLs to validate causal gene assignments.
Understand how to predict the direction of genetic effects, such as whether a metabolite is a substrate or a product of an enzyme, based on the relative abundance of proteins and metabolites.
Learn practical expectations for interpreting GWAS data by applying known biochemical pathways and protein-metabolite colocalization principles.
Speaker:
Eric Fauman, PhD.
Executive Director of AI Strategy and Computational Biology, Pfizer
Eric Fauman, PhD. is Executive Director of AI Strategy and Computational Biology at Pfizer. With a career spanning structural biology, computational biology, human genetics, and AI‑enabled drug discovery, he has spent nearly three decades translating computational advances into decision‑relevant biological insight. His perspective emphasizes the evolving role of the computational biologist as a scientific interpreter, validator, and steward of trust as biology enters an AI‑first era.